I seek to examine the risk relationship between a sample of renewable and conventional energy equity indexes. My sample consists of fourteen renewable energy indexes for which I collect monthly total return data from three different data sources, Thomson Reuters Datastream, Global Financial Data (GFD), and Ardour Global (Alternative Energy Indexes). I restrict my attention to energy indexes which track the performance of energy producing firms, specialised in renewable energy (such as solar, wind, and hydro), alternative fuels (bio- fuels) and related renewable technologies (such as storage and efficiency of renewable energy). In defining whether a company qualifies as renewable, the index providers employ the following three screens: (i) sector, (ii) renewable income, and (iii) liquidity. The sector screen is to rule out any firms not having any business activities related to alternative energy firms. The renewable income screen is to ensure that companies generate sufficient revenues or net income from renewable business activities to be classified as such. In addition, renewable energy index providers use a liquidity screen, either a liquidity ratio (calculated as average 3-month daily trading volume divided by the average 3-month market capitalisation) or minimum trading volume and market capitalisation, to screen out less frequently traded firms.
Panel A of Table 13 summarises how and to what extent each renewable energy index uses the screens. Sector screens vary to some extent. For example, indexes with global investment objectives tend to be broader and less selective in their inclusion of energy businesses. This contrasts with more specialised indexes such as NASDAQ Renewable Edge index, HFRX Alternative Energy, Ardour Global Alternative Energy Solar and S&P Asia Alternative Energy. Table 13 shows that the majority of indexes use a renewable income threshold of over 50 percent, except for Ardour Global Alternative Energy Solar who uses 66 percent of gross revenues or net income. Finally, approximately half of the sample uses minimum average trading volumes of 1 million USD and average free-float market capitalisations of 100 million USD. The sample of renewable energy indexes tracks the
performance of renewable energy firms in various regions such as Asia, Europe, US and Worldwide.
Panel A of Table 13 also presents annualised summary statistics for the full sample period of fourteen renewable energy indexes. I find annualised average returns of ten renewable energy indexes to be negative. This is in line with previous studies (Bohl et al., 2013).
However, DaxGlobal Alternative, S&P Global Alternative, NASDAQ Renewable Edge and European Renewable Energy, generate low, but positive annualised average returns. Although absolute positive performances of these four renewable energy indexes are not dominated by one specific region (one European, one US and two Global indexes), it appears that specific sub-segments of renewable energy are much more attractive than others. I argue that the observed positive performance specifically relates to the industry rather than to geography. To give one example, the pure solar energy index (AGAE Solar) produced the lowest annualised return combined with the highest volatility and the worst loss. In contrast, the more specific and technology focused NASDAQ Renewable Edge generates, on average, a positive return with the lowest maximum loss and volatility. This finding is in line with that of Statman (2006), who compared socially responsible with conventional indexes.
Total volatility, proxied by the annualised standard deviation, shows that all renewable energy indexes are volatile investments indeed. Annualised standard deviations range from 35 to 40 percent. In contrast, standard deviations for conventional oil and gas indexes cluster at around 15 to 30 percent (see Panel B of Table 13). Annualised semi- standard deviations and lower partial moments are on average 7 to 10 percent higher than for traditional energy companies. Previous studies confirm high volatilities and Sadorsky highlights this by pointing out that "Renewable Energy companies are often among the riskiest types of companies to invest in" (Sadorsky, 2012b:39). They are risky for mainly two reasons. First, renewable energy companies tend to be small start-up types of businesses that concentrate their resources to develop one specific type of renewable energy technology. From an investor perspective, the uncertainty attached to one project is very high, as its success or failure wholly depends on individual projects. Adding to uncertainty, it is questionable which of the alternative energy technologies will penetrate the market in the future. Second, due to relatively high up-front investment costs, the renewable energy sector has received governmental support in various industrial countries (Morris et al., 2012). One example is the European solar sector, where the dominant method of subsidies are "feed-in-
policy investments" (Lüthi and Wüstenhagen, 2012: 1001). Notable risks of policy investing are the administrative process, policy stability, and support duration. Thus, it is not surprising that high uncertainty about the outcome of policy investing requires a risk premium by investors and justifies my findings of high absolute volatilities across different renewable energy indexes. Descriptive statistics such as the third and fourth moments show additional characteristics of renewable energy return distributions. The sample skewness is about -1 for all renewable energy indexes and indicates return distributions skewed to the left. The sample Kurtosis strongly exceeds three, which implies fat-tailed distributions. I reject the hypothesis of normality from the Jarque-Bera test statistic for all returns of the renewable indexes.
Table 13: Renewable and Conventional Energy Sample and Summary Statistics
ID Clean Energy Index From To Obs
Ann. Mean
Ann. Median
Ann.
Std.Dev Jarque- Bera Region Clean Income Screen Liquidity Screen Index Impurity
Income Ratio (Ann.) Liquidity Ratio^ Trading Volume (Min) Market Cap (Min) % of Non-Renewable firms listed
Panel A: Renewable Energy
C1 Ardour Global Alt. Energy 31/12/1999 28/02/2013 158 -0.0515 0.1755 0.3795 66.47*** World > 50% of gross revenues >25% 6%
C2
Ardour Global Alt. Energy Extra
Liq. 31/01/2000 28/02/2013 157 -0.0790 0.1058 0.3869 82.58*** World > 50% of gross revenues >25% 13%
C3
Ardour Global Alt. Energy N.
America 31/12/1999 28/02/2013 158 -0.0737 0.0019 0.3977 25.20*** N-America > 50% of gross revenues >25% 7%
C4 Ardour Global Alt. Energy Europe 30/06/2005 28/02/2013 92 -0.0950 0.1371 0.4063 56.24*** Europe > 50% of gross revenues >25% 0%
C5 Ardour Global Alt. Energy Solar 31/12/2004 28/02/2013 98 -0.1086 0.2196 0.5463 22.26*** World > 66% of gross revenues > $1 million 0%
C6 Daxglobal Alternative Energy 29/12/2000 28/02/2013 146 0.0114 0.1545 0.2839 20.40*** World > 50% of gross revenues $1.2 million $150 million 33%
C7
World Renewable Energy
(Renixx) 31/01/2002 28/02/2013 133 -0.1567 0.1251 0.3943 26.92*** World > 50% of gross revenues
Highest f-f mkt.
cap. 7%
C8 S&P Global Alternative Energy 28/11/2003 28/02/2013 111 0.0198 0.2274 0.2747 134.20*** World > 50% of gross revenues $3 million $300 million 46%
C9 S&P Asia Alternative Energy 30/06/2008 28/02/2013 56 -0.1302 -0.0810 0.3570 5.96* Asia Not available > $2 million^^ > $250 million 45%
C10 HFRX Alternative Energy 31/01/2006 28/02/2013 111 -0.0714 0.2146 0.3618 181.09*** World Not available Not available Not available Not available
C11 NASDAQ Renewable Edge US Liq. 30/11/2006 28/02/2013 85 0.0328 0.0723 0.1772 99.08*** US > 50% of gross revenues 100,000 shares $150 million 0%
C12 S&P Global Renewable Energy 28/11/2003 28/02/2013 75 -0.0880 0.2122 0.3569 28.60*** World > 50% of gross revenues $3 million $300 million 18%
C13 European Renewable Energy 30/09/2003 31/01/2012 100 0.0108 0.1831 0.4110 85.21*** Europe > 50% of gross revenues 10 largest in sector
10 largest in
sector 0% C14 Wilderhill New Energy Global Inn. 29/12/2000 28/02/2013 146 -0.0090 0.1638 0.2976 140.73*** US
> 10% to > 50% of market
value $1 million $100 million** 8%
Panel B: Conventional Energy
D1
MSCI World Oil, Gas & Cons.
Fuels 30/12/1994 28/02/2013 158 0.0824 0.1196 0.1982 5.61* World
D2 FTSE World Oil & Gas 30/12/1992 28/02/2013 158 0.0857 0.1214 0.2042 5.06* World
D3
ThomsonReuters Global Oil &
Gas 30/12/1992 28/02/2013 158 0.0762 0.1234 0.1984 5.55* World
D4 Dow Jones Titans Oil & Gas 30 30/12/1991 28/02/2013 158 0.0761 0.1239 0.2108 5.92* World D5 MSCI World Metals & Mining 30/12/1994 28/02/2013 158 0.0803 0.1721 0.2926 143.83*** World D6 S&P 500 Oil, Gas & Cons. Fuels 30/09/1989 28/02/2013 158 0.0778 0.0865 0.1948 6.83** US
D7 Dow Jones US Int. Oil & Gas 29/02/1992 28/02/2013 158 0.0687 0.0540 0.1836 2.65 US
D8 NYSE Arca Oil 16/11/1984 28/02/2013 158 0.0734 0.1157 0.2191 2.94 US
Table 13: Renewable and Conventional Energy Sample and Summary Statistics
ID Clean Energy Index From To Obs
Ann. Mean
Ann. Median
Ann.
Std.Dev Jarque- Bera Region Clean Income Screen Liquidity Screen Index Impurity
Income Ratio (Ann.) Liquidity Ratio^ Trading Volume (Min) Market Cap (Min) % of Non-Renewable firms listed D13 Daxglobal Nuclear Energy Perf. 28/09/2001 28/02/2013 137 0.0024 0.0543 0.2621 1.52 World
D14 Daxglobal Asia Oil & Gas Perf. 28/09/2001 28/02/2013 137 0.1773 0.0529 0.2714 209.61*** Asia D15 DJGL Asia Pac. Dev. Int. Oil & Gas 30/03/2001 28/02/2013 143 0.0121 0.0118 0.3292 1.54 Asia D16 DJGL Asia Pac. Dev. Oil & Gas 30/03/2001 28/02/2013 158 0.1198 0.1678 0.2740 83.96*** Asia
D17 HFRX EH Energy Basic Materials 31/01/2005 28/02/2013 97 0.0423 0.0762 0.1377 79.03*** World
D18 NASDAQ/SIG Oil Explor. & Prod. 28/06/2005 28/02/2013 92 0.0849 0.2153 0.3354 7.52** US
Notes: This table reports descriptive statistics and general information for each of the fourteen renewable and eighteen conventional energy indexes. The first two columns display my ID and full index name. The next three columns indicate data availability. Columns 6 to 9 display descriptive statistics. I compute descriptive statistics over the sample period December 1999 to February 2013. Note that mean, median, and standard deviation are annualized numbers. I annualize my monthly estimates by multiplying that number with twelve. Standard deviations are multiplied with the square root of twelve. Column 10 represent investmen regions of each index. The next four columns summarise three most frequently used screens for identifying eligible energy companies by clean energy index providers. First, index providers calculate clean income screen as the annual income ratio of gross revenues from renewable sources to total gross revenues. At least 50 percent of a companies' income has to be generated through clean business activities. Second, liquidity screens are based on either liquidity ratios (trading volume to market capitalization) or a combination of minimum trading volume and market capitalization. Third, sector screens exclude companies with activities other than sourcing energy from renewable activities. All three screens are equally important in excluding non-compliant companies. The final column shows whether and to what extent the indexes are "pure play". For this, I manually check the holdings of each index and count companies that are considerably or partially operating in the coal, metals & mining, nuclear, and oil & gas sector. I report proportions of impure companies to total companies in each respective index. ^ Defined as average 3-month daily trading volume divided by the average 3-month market capitalization in USD. ^^ Three-month average market capitalisation. ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively.